iDatamining.org

Friday, September 16, 2016

Trump vs Hillary on Twitter.

WIKIleaks dumps massive new DNC leak on Sept 13th, 2016 (see one video on Youtube). It is obvious that both campaigns are using public media to gain more support. Here, I have something interesting to reveal too.

What I did are,

1) Using two group of hashtags, which are assumed to support Hillary or Trump respectively (not 100% accurate, but should be with high accuracy rate),

2) Writing an app to listen to Twitter events that will be triggered when any above hashtag is posted on Twitter.

3) Using descriptive statistics methods to do some analysis. Possibly, we can do more advanced analysis on the data. I am looking for Opinion mining training data for Twitter or other social media. Please let me know if you happen to have it and will share it with me.

My app has been continuously running and collecting data for more than two weeks now. I analyzed the first week's data and put some result here. It looks like both Trump supporters and Hillary supporters have been working hard on Twitter and some automation post applications are used to post tweets on Twitter. It also looks like Trump side know more about how to use social media auto app than Hillary side.

Let's see in one week, each candidate has how many supporters on the twitter.

Obviously, Trump supporters work much harder than Hillary's supporters on Twitters. Data are collected from Sept. 5 (Monday) to Sept. 11 (Sunday). You can also see the peak time of tweets post are on Thursday and Saturday. I will continuously observe if the is a really behavior pattern.

Let's see what time in a day has the most tweets screaming on the twitter.

It is interesting to see, during one day, if people tweets at certain time of a day. For example, lunch time? dinner time? Below picture shows a clear pattern of user behavior. it looks like people like to post tweets at UTC time 5pm (1pm EST) and 24pm (8pm EST).
This pattern could be useful for both Hillary and Trump campaigns to effectively post on social medial like twitter. And, in the fact, I think they do it already. I will show evidence in the following analysis.

How different language speaking people support Hillary or Trump?

We filter out those languages that has less than 200 post in a week and get below picture.
It is clear that Hillary has more support only from es (Spanish) and "unknown". On the other hand, Trump get more support from people who speaks British English (en-gb), French (fr), German (de) and all the rest. People, who is interested in finding out all languages listed, can check language code by click here.

What kind devices/application each side use mostly?

After investigating the language people use, we can have a look at what application/devices users use to post tweet. The interesting phenomena is that Twitter Web Client contributes the most tweets.

Since there are too many different application and devices are used to post on twitter, I just plot two different data as below. I sorted largest number of tweets for each application/device. Not surprisingly, Hillary side and Trump side have different top 20 devices that post tweets. So, I plot two different pictures, one is for Hillary side's top 20 and the other is for Trump side's top 20. You can compare listed applications and see what is shown in Trump's side not in Hillary side. It will be interesting to dig more with those application.

For Hillary's top 20 applications,

For Trump's top 20 applications

Let's list out top used devices/app for Trump and Hillary respective. Then, you can align them and carefully compare both sides. Clearly, Trump side attracts more auto application to tweets. On the Hillary side, one stands out application is "Monkey Thank U". So, who are they? What do they post on twitter? You can explore them with data I share at the bottom of this post.

Hillary

Trump

Application

Num

1

Twitter Web Client

181518

2

Twitter for iPhone

161013

3

Twitter for Android

151083

4

Twitter for iPad

62717

5

Monkey Thank u

10782

6

Mobile Web (M5)

10512

7

TweetDeck

9591

8

RoundTeam

5058

9

Annie Green 1.0

4708

10

diana aquaviva 1.0

4161

11

http://ussanews.com/news1/

4040

12

Hootsuite

2839

13

Twitter for Windows Phone

2717

14

http://nyc.epeak.in/

2679

15

prohiggins1.0

2678

16

Twitter for Windows

2476

17

DropOuthillary1

2252

18

DeviationStand 1.0

2239

19

Daniel Addison 1.0

2232

20

zerosum 1.0

2206

Application

Num

1

Twitter Web Client

995545

2

Twitter for iPhone

922009

3

Twitter for Android

830775

4

Twitter for iPad

282343

5

Mobile Web (M5)

72408

6

Zapier.com

61257

7

IFTTT

32233

8

Tweet Jukebox

26551

9

Twitter for Windows

16821

10

TweetDeck

13780

11

RoundTeam

11398

12

PinkPoniesGreat

10120

13

Mobile Web (M2)

9350

14

Hootsuite

8373

15

StopMadness

7841

16

Twitter for Windows Phone

7800

17

thebestappsever

7644

18

oneoftheapps

7437

19

rollingtwitter

6585

20

WhytePantherTest1

6337

Let's explore a little bit what both sides' robot post on the twitter.

I lived in China before 1999. Since 1996, I had been working as a software R&D engineer. I worked on JavaOS, a lighweight network operating system for Network Computer. Meanwhile, I used CGI, applet, swing, activeX control, and Webshpere 1.0 to develop banking Web applications.

1999~2010. I immigrated to Singapore and worked on core Java applications, Web applications, mobile applications, and interactive TV middleware and applications. Singapore feeds me more than 10 years. I appreciate her!

2010 ~ present. I moved to USA to reunion with my family. I am currently living in great Boston area. I'd like to make more friends here. Nice to meet you :)